Evolving color recipes

This paper highlights an evolutionary computing intelligence for a computer ized color recipe prediction that requires function approximation and combi natorial solution of colorants to produce color recipes for a given target color sample. We attack this real challenging problem in the color (paint) industry by using an evolutionary computing system that consists of a probl em-specific knowledge and three principal constituents of soft-computing: n eural networks, a fuzzy system, and a genetic algorithm. Departing from the recipe results obtained by neural networks (NN) approaches, the evolutiona ry system attempts to improve them In conjunction with fuzzy classification , a knowledge base and neural fitness functions. All components function sy nergistically in obtaining precise color recipe outputs through simulation of color paint manufacturing process. Such computational intelligence can b e useful, especially when an exact mathematical model of the real-world pro cess under consideration is not available explicitly.